Dual Task Framework for Improving Persona-Grounded Dialogue Dataset
DOI:
https://doi.org/10.1609/aaai.v36i10.21338Keywords:
Speech & Natural Language Processing (SNLP)Abstract
This paper introduces a simple yet effective data-centric approach for the task of improving persona-conditioned dialogue agents. Prior model-centric approaches unquestioningly depend on the raw crowdsourced benchmark datasets such as Persona-Chat. In contrast, we aim to fix annotation artifacts in benchmarking, which is orthogonally applicable to any dialogue model. Specifically, we augment relevant personas to improve dialogue dataset/agent, by leveraging the primal-dual structure of the two tasks, predicting dialogue responses and personas based on each other. Experiments on Persona-Chat show that our approach outperforms pre-trained LMs by an 11.7 point gain in terms of accuracy.Downloads
Published
2022-06-28
How to Cite
Kim, M., Kwak, B.- woo, Kim, Y., Lee, H.- in, Hwang, S.- won, & Yeo, J. (2022). Dual Task Framework for Improving Persona-Grounded Dialogue Dataset. Proceedings of the AAAI Conference on Artificial Intelligence, 36(10), 10912-10920. https://doi.org/10.1609/aaai.v36i10.21338
Issue
Section
AAAI Technical Track on Speech and Natural Language Processing